{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Fun_with_tensorflow_probability.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "kUKZqqQvlAoe"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import tensorflow_probability as tfp\n",
        "import functools, inspect, sys\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tfd = tfp.distributions\n",
        "distribution_class =  tfp.distributions.Distribution\n",
        "distributions = [name for name, obj in inspect.getmembers(tfd)\n",
        "                if inspect.isclass(obj) and issubclass(obj, distribution_class)]\n"
      ],
      "metadata": {
        "id": "6QPY_edTlIzw"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(distributions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBehcKntmJRx",
        "outputId": "29a9b3d0-eba6-4b03-f9c6-4131b5f16cfd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Autoregressive', 'BatchBroadcast', 'BatchConcat', 'BatchReshape', 'Bates', 'Bernoulli', 'Beta', 'BetaBinomial', 'BetaQuotient', 'Binomial', 'Blockwise', 'Categorical', 'Cauchy', 'Chi', 'Chi2', 'CholeskyLKJ', 'ContinuousBernoulli', 'DeterminantalPointProcess', 'Deterministic', 'Dirichlet', 'DirichletMultinomial', 'Distribution', 'DoublesidedMaxwell', 'Empirical', 'ExpGamma', 'ExpInverseGamma', 'ExpRelaxedOneHotCategorical', 'Exponential', 'ExponentiallyModifiedGaussian', 'FiniteDiscrete', 'Gamma', 'GammaGamma', 'GaussianProcess', 'GaussianProcessRegressionModel', 'GeneralizedExtremeValue', 'GeneralizedNormal', 'GeneralizedPareto', 'Geometric', 'Gumbel', 'HalfCauchy', 'HalfNormal', 'HalfStudentT', 'HiddenMarkovModel', 'Horseshoe', 'Independent', 'InverseGamma', 'InverseGaussian', 'JohnsonSU', 'JointDistribution', 'JointDistributionCoroutine', 'JointDistributionCoroutineAutoBatched', 'JointDistributionNamed', 'JointDistributionNamedAutoBatched', 'JointDistributionSequential', 'JointDistributionSequentialAutoBatched', 'Kumaraswamy', 'LKJ', 'LambertWDistribution', 'LambertWNormal', 'Laplace', 'LinearGaussianStateSpaceModel', 'LogLogistic', 'LogNormal', 'Logistic', 'LogitNormal', 'MarkovChain', 'Masked', 'MatrixNormalLinearOperator', 'MatrixTLinearOperator', 'Mixture', 'MixtureSameFamily', 'Moyal', 'Multinomial', 'MultivariateNormalDiag', 'MultivariateNormalDiagPlusLowRank', 'MultivariateNormalDiagPlusLowRankCovariance', 'MultivariateNormalFullCovariance', 'MultivariateNormalLinearOperator', 'MultivariateNormalTriL', 'MultivariateStudentTLinearOperator', 'NegativeBinomial', 'Normal', 'NormalInverseGaussian', 'OneHotCategorical', 'OrderedLogistic', 'PERT', 'Pareto', 'PixelCNN', 'PlackettLuce', 'Poisson', 'PoissonLogNormalQuadratureCompound', 'PowerSpherical', 'ProbitBernoulli', 'QuantizedDistribution', 'RelaxedBernoulli', 'RelaxedOneHotCategorical', 'Sample', 'SigmoidBeta', 'SinhArcsinh', 'Skellam', 'SphericalUniform', 'StoppingRatioLogistic', 'StudentT', 'StudentTProcess', 'StudentTProcessRegressionModel', 'TransformedDistribution', 'Triangular', 'TruncatedCauchy', 'TruncatedNormal', 'Uniform', 'VariationalGaussianProcess', 'VectorDeterministic', 'VonMises', 'VonMisesFisher', 'Weibull', 'WishartLinearOperator', 'WishartTriL', 'Zipf']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal = tfd.Normal(loc=0., scale=1.)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BvTVwJ4RmMB8",
        "outputId": "ef5dad23-f871-4173-ff5c-54ad1ea10412"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tfp.distributions.Normal(\"Normal\", batch_shape=[], event_shape=[], dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_normal(N):\n",
        "  samples = normal.sample(N)\n",
        "  sns.distplot(samples)\n",
        "  plt.title(f\"Normal Distribution with zero mean, and 1 std. dev {N} samples\")\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "gPgglzwh7o9F"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for n in [100, 1000, 10000]:\n",
        "  plot_normal(n)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "id": "pIj1KDnepy7y",
        "outputId": "e86268f3-f440-4710-b35f-f9abb3d16b65"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "A11uYBXQqmk9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}